GeneTS: A Relational-Functional Genetic Algorithm for the Traveling Salesman Problem

Abstrakt

This work demonstrates a use of the relational-functional language RelFun for specifying and implementing genetic algorithms. Informal descriptions of the traveling salesman problem and a solution strategy are given. From these a running RelFun application is developed, whose most important parts are presented. This application achieves good approximations to traveling salesman problems by using a genetic algorithm variant with particularly tailored data representations. The feasibility of implementing sizable applications in RelFun is discussed.